Maize plant height automatic reading of measurement scale based on improved YOLOv5 lightweight model.

Abstract

Background Plant height is a significant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms. Method This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales. Results The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm’s 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm’s results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.

Plant phenotyping relevance

トウモロコシの草丈という植物形質を画像・物体検出で自動推定する手法を開発し、既存モデルとの比較および手動測定による検証を行っており、フェノタイピング手法が中心である。

Code and data availability

The authors deposited the paper's maize plant height measuring scale image dataset on figshare, explicitly linked in the Data Availability statement. Supplemental detection code and model configuration exist but their URLs are not among the allowed URLs, so only the figshare dataset is reported.